The Role of Artificial Intelligence in Deep Brain Stimulation

Tejas R. Mehta; Venkat S. Lavu; Hao Gao; Tania Banerjee; Renjie Hu; Ruogu Fang; Joshua K. Wong

doi:10.1055/a-2753-6166

Seminars in Neurology, Table of Contents

Semin Neurol
DOI: 10.1055/a-2753-6166

Review Article

The Role of Artificial Intelligence in Deep Brain Stimulation

Authors

Tejas R. Mehta

¹Department of Neurology, Norman Fixel Institute for Neurological Disease, University of Florida, Gainesville, Florida, United States
Venkat S. Lavu

²Department of Neurology, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States
Hao Gao

³Department of Medicine, King's College Hospital, Denmark Hill, London, United Kingdom
Tania Banerjee

⁴Department of Information Science Technology, Cullen College of Engineering, University of Houston, Houston, Texas, United States
Renjie Hu

⁴Department of Information Science Technology, Cullen College of Engineering, University of Houston, Houston, Texas, United States
Ruogu Fang

⁵J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, Florida, United States
Joshua K. Wong

¹Department of Neurology, Norman Fixel Institute for Neurological Disease, University of Florida, Gainesville, Florida, United States

Abstract

Deep brain stimulation (DBS) is a highly effective treatment for movement disorders like Parkinson's disease, essential tremor, and dystonia. However, the current multidisciplinary workflow for implanting and programming DBS is often complex, which can lead to improperly placed leads, suboptimal symptom management, and increased procedure time, ultimately resulting in poor patient outcomes. There is a pressing need for a more streamlined, accurate, reproducible, and personalized approach to DBS therapy. Artificial intelligence (AI), which can analyze complex data and identify patterns with remarkable speed, holds significant promise as a tool to address these challenges. This narrative review explores the current and future applications of AI in improving the entire DBS workflow, from surgical planning and lead placement to postoperative programming, with the goal of enhancing clinical efficiency and achieving better, more personalized outcomes for patients with movement disorders.

Keywords

DBS - AI - ML - DL - imaging

Full Text

References

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